เปรียบเทียบวิธี

	Bayesian Multidimensional Scaling (BMDS)	การวิเคราะห์กลุ่มแบบเบย์ (Bayesian Cluster Analysis)
สาขาวิชา	สถิติศาสตร์	สถิติศาสตร์
ตระกูล	Latent structure	Latent structure
ปีกำเนิด	2001	1998–2002
ผู้ริเริ่ม	Oh & Raftery	Fraley & Raftery (model-based); Dirichlet process formulations by Ferguson (1973) and Antoniak (1974)
ประเภท	Bayesian latent-space dimensionality reduction	Probabilistic / model-based clustering
แหล่งต้นตำรับ	Oh, M.-S. & Raftery, A. E. (2001). Bayesian multidimensional scaling and choice of dimension. Journal of the American Statistical Association, 96(455), 1031–1044.	Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631.
ชื่อเรียกอื่น	Bayesian MDS, BMDS, probabilistic MDS, Bayesian proximity scaling	BCA, Bayesian clustering, probabilistic cluster analysis, Bayesian model-based clustering
ที่เกี่ยวข้อง	6	6
สรุป	Bayesian Multidimensional Scaling places objects in a low-dimensional latent space so that inter-object distances reproduce observed dissimilarities, while a full Bayesian treatment quantifies uncertainty in the coordinates, handles missing proximities naturally, and selects the number of dimensions via model comparison rather than heuristic inspection.	Bayesian cluster analysis assigns observations to latent groups by combining a probabilistic model of within-cluster data with prior beliefs about cluster parameters and the number of clusters. It yields posterior probabilities of cluster membership and principled uncertainty estimates, making it more transparent than classical distance-based clustering algorithms.